FADIA

Remote attestation has emerged as a valuable security mechanism which aims to verify remotely whether or not a potentially untrusted device has been compromised. The protocols of Remote attestation are particularly important for securing Internet of Things (IoT) systems which, due to the large number of interconnected devices and limited security protections, are susceptible to a wide variety of cyber attacks. To guarantee the integrity of a software running on a single device, remote attestation is usually executed as an uninterrupted procedure: at the attestation time, a device stops the normal operation and executes the attestation of the entire device without interruption. The remote attestation protocols that aim to attest a large number of devices also follow the assumption on uninterrupted execution: when a device attests its network neighbours, each device verified in the neighborhood suspends its normal operation until the attestation protocol is completed. To avoid unnecessary suspension of the normal operation of the devices, this paper proposes a novel Secure Asynchronous Remote Attestation (SARA) protocol that releases the constraint of synchronous interaction among devices. In particular, SARA is an attestation protocol that exploits asynchronous communication capabilities among IoT devices in order to attest a distributed IoT service executed by them. SARA verifies both that each IoT device is not compromised (device trustworthiness), and that the exchanged communication data have not maliciously influence the communicating devices (legitimate operations). By tracing the execution order of each service invocation of an asynchronous distributed service, SARA allows each service to collect accurately historical data of its interactions, and transmits asynchronously such historical data to other interacting services. We have implemented and validated SARA through a realistic simulation on the Contiki emulator that demonstrates the functionality and efficiency of our protocol. The results confirm the suitability of SARA for low-end devices.

Remote attestation (RA) is a security mechanism that verifies the trustworthiness of remote IoT devices. Traditional RA protocols aim to detect the presence of malicious code in the static memory of a device. In the IoT domain, RA research is currently following two main directions: Dynamic RA and Swarm RA. Dynamic RA schemes intend to detect runtime attacks that hijack the control-flow execution of a running program without injecting new malicious code into the memory. On the other hand, swarm RA protocols focus on attesting efficiently and effectively a large number of IoT devices. However, existing RA protocols do not perform dynamic attestation in asynchronous IoT networks. This paper proposes a RA protocol for Asynchronous Remote Control-Flow Attestation of Distributed IoT Services (ARCADIS). This protocol extends the state-of-the-art by detecting IoT devices that have (directly or indirectly) been maliciously influenced by runtime attacks on asynchronous distributed IoT services. The protocol has been simulated for Wismote sensors in the Contiki emulator. The conducted experiments confirm the feasibility of ARCADIS and demonstrate its practicality for small IoT networks.

Remote Attestation (RA) has gained a broad attention over recent years as an essential security mechanism that enables integrity verification of remote IoT devices. Typically, existing RA protocols aim at detecting malware presence in program memory. Recent RA schemes work towards attesting also data memory and focus mainly on detecting runtime attacks that manipulate stack pointers to hijack the execution flow of a running program. Despite different RA approaches, some data memory attacks still remain undetected. This paper proposes ERAMO, a novel RA protocol that investigates memory offloading technique in attesting broad memory regions of IoT devices. Instead of running a complex RA protocol on a resource-constrained IoT device, ERAMO leverages the emerging paradigm of Fog Computing to securely offload memory contents of IoT devices to nearby powerful devices. This approach aims at increasing the effectiveness of RA protocols by attesting larger data memory regions and allowing powerful devices to perform complex analysis of IoT devices’ state. We validate and evaluate ERAMO with a hardware proof-of-concept implementation using an ARM Cortex-M33 based microcontroller that provides ARM TrustZone to support secure isolation of the RA procedure. The conducted experiments confirm the feasibility of ERAMO and demonstrate that offloading technique increases the RA effectiveness in attesting dynamic memory regions.

Remote attestation is a security technique through which a remote trusted party (i.e., Verifier) checks the trust-worthiness of a potentially untrusted device (i.e., Prover). In the Internet of Things (IoT) systems, the existing remote attestation protocols propose various approaches to detect the modified software and physical tampering attacks. However, in an inter-operable IoT system, in which IoT devices interact autonomously among themselves, an additional problem arises: a compromised IoT service can influence the genuine operation of other invoked service, without changing the software of the latter. In this paper, we propose a protocol for Remote Attestation of Distributed IoT Services (RADIS), which verifies the trust-worthiness of distributed IoT services. Instead of attesting the complete memory content of the entire interoperable IoT devices, RADIS attests only the services involved in performing a certain functionality. RADIS relies on a control-flow attestation technique to detect IoT services that perform an unexpected operation due to their interactions with a malicious remote service. Our experiments show the effectiveness of our protocol in validating the integrity status of a distributed IoT service.

As the number of Internet of Things (IoT) devices and applications increases, the capacity of the IoT access networks is considerably stressed. This can create significant performance bottlenecks in various layers of an end-to-end communication path, including the scheduling of the spectrum, the resource requirements for processing the IoT data at the Edge and/or Cloud, and the attainable delay for critical emergency scenarios. Thus, a proper classification or prediction of the time varying traffic characteristics of the IoT devices is required. However, this classification remains at large an open challenge. Most of the existing solutions are based on machine learning techniques, which nonetheless present high computational cost, whereas they are not considering the fine-grained flow characteristics of the traffic. To this end, this paper introduces the following four contributions. Firstly, we provide an extended feature set including, flow, packet and device level features to characterize the IoT devices in the context of a smart environment. Secondly, we propose a custom weighting based preprocessing algorithm to determine the importance of the data values. Thirdly, we present insights into traffic characteristics using feature selection and correlation mechanisms. Finally, we develop a two-stage learning algorithm and we demonstrate its ability to accurately categorize the IoT devices in two different datasets. The evaluation results show that the proposed learning framework achieves 99.9% accuracy for the first dataset and 99.8% accuracy for the second. Additionally, for the first dataset we achieve a precision and recall performance of 99.6% and 99.5%, while for the second dataset the precission and recall attained is of 99.6% and 99.7% respectively. These results show that our approach clearly outperforms other well-known machine learning methods. Hence, this work provides a useful model deployed in a realistic IoT scenario, where IoT traffic and devices’ profiles are predicted and classified, while facilitating the data processing in the upper layers of an end-to-end communication model.

The Internet of Things (IoT) devices is able to communicate with another IoT devices in heterogeneous environments. They are also able to create a network by their own using wireless capabilities without a centralized approach. This network is called wireless mobile ad hoc network (MANET). The MANET facilitate IoT devices to communicate with each other in an infrastructure less network of the Internet of Things environment. The IoT devices can dynamically join, create connection and exchange information among them. The role of cloud in this framework is to store and process the data by IoT devices. The communication Security has been raised as one of the most critical issues of cloud computing where resolving such an issue would result in a constant growth in the use and popularity of cloud. Our purpose of this study is to create a framework for providing the communication in an integrated framework of Cloud and MANET among IoT devices. Our main contribution links a new study that integrates cloud computing to the MANET and provides communication to the internet of things. This study can be used in the framework of the Internet of Things in future.

The Internet of Things (IoT) has had a considerable influence on our daily lives by enabling enhanced connection of devices, systems, and services that extends beyond machine-to-machine interactions and encompasses a wide range of protocols, domains, and applications. However, despite privacy concerns shown by IoT users, little has been done to reduce and protect individual information exposure. It is extremely difficult to mitigate IoT devices from reidentification threats which is why it is still a major challenge for IoT users to securely protect their information. The trust controls how we regulate privacy in our IoT platforms in the same way that it governs personal relationships. As IoT devices become increasingly linked, more data is shared across individuals, businesses, governments, and ecosystems. Technologies, sensors, machines, data, and cloud connections all rely largely on trust relationships that have been formed. With the rapid growth of additional types of IoT devices that are being introduced, it, therefore, expands privacy concerns and is difficult to develop trust with an IoT system or device without the option to regulate information privacy settings. Privacy has always been a barrier for many devices as they race for the early adoption of IoT technologies. Several Internet of Things devices or systems will continue to pose privacy threats. As a result, the main objective of this study was to examine the individual understanding of privacy and to promote information privacy protection awareness not only to IoT users but also to organizations that use IoT devices or platforms to run their day-to-day business operations. Furthermore, the objective extends to compare user knowledge and concerns about IoT privacy, as well as to identify any common attitudes and variances. However, in terms of enhancing individuals’ knowledge, an artifact was developed to educate and enhance information privacy awareness among IoT users. A pre- and postquestionnaire was generated to test and validate user knowledge regarding information privacy protection in IoT. The study was conducted using a quantitative research method. Findings indicate that IoT users’ awareness of information privacy protection turned out to be average, suggesting a need for education and awareness. Several participants stated that information privacy protection awareness is required within the community to educate, raise awareness, eliminate human error, and enable individuals to be conscious of their privacy when surfing the Internet.

ABSTRACTIn recent years, the emerging development of Internet of things (IoT) has created new opportunities for IoT service provider, device manufacturer and customer (end user). In agriculture, employing IoT can help reducing manpower as well as standardizing agricultural processes, thus increasing the benefit for customer who does agricultural business. Beside the opportunities, there are also issues while doing maintenance for an agricultural IoT system. One of them is how to automate the process to detect faults of IoT devices in a large deployment area. While previous studies focus on investigating algorithms as well as theoretical model to detect faults for specific IoT devices, automatically expanding of IoT management process for detecting faults of sensor/actuator has not been investigated yet. To achieve this, we should be able to deal with the relationship between IoT device manufacturer, service provider and customer. In this study, we conceptualize the knowledge of IoT devices, management process and fault detection requirement. To realize this model, we introduce a runtime system to expand the IoT management process for detecting sensor/actuator faults which is able to provide the above features. We do several experiments to show the effectiveness and usefulness of the proposed solution.

Seeing is not always believing: Insights on IoT manufacturing from firmware composition analysis and vendor survey

With the advancement in science and technology, the demand for electronic Internet of Things (IoT) devices has increased to store and process digital data. With the advancement in IoT technology, we are more reliant on IoT devices to perform our regular tasks. IoT Forensics deals with the investigation of IoT devices such as smartphones, tablets, smart watches, or any device which is connected to the Internet. The data on these devices can still be retrieved via IoT forensics even if they are destroyed, resold, or misplaced. Before discarding any IoT device, we must consider how to secure the data stored on them. We are then going to discuss the several types of personal data that could be retrieved from discarded IoT devices. Finally, we are going to discuss the protection measures for the safe disposal of IoT devices.

The number of Internet of Things (IoT) devices has increased recently and will increase further in the future. IoT devices are vulnerable, so vendors update and distribute firmware. At the time of firmware distribution, a huge number of IoT devices may connect to the firmware server and excessive network traffic may occur. Consequently, a method using a peer-to-peer file sharing system and a blockchain has been proposed. One study proposed an update scheme considering incentives for distributors to help with updating. However, this method requires the IoT devices, which have limited processing power, to decrypt encrypted files, and the distribution nodes to pay the transaction cost. This paper proposes a firmware distribution method that provides incentives for distributors to help with distribution to reduce the gas costs, using a smart contract and access control based on update records. The additional computations performed by IoT devices and distributors key management were reduced compared to those in previous studies by using access control instead of encryption. Further, the proposed approach was implemented and evaluated using a Raspberry Pi and laptop. The evaluation demonstrated that the gas cost per update was successfully lowered compared to the costs in previous studies.

As location-aware IoT (Internet of Things) devices intend to spread across almost every application, IoT devices are going to require different GNSS (Global Navigation Satellite System) receiver design requirements. To deliver on IoT opportunities, system architects and designers need to look carefully at every aspect of system design and architecture to be able to enable the full potential of the IoT era. IoT devices are equipped with different connectivity sensors that can provide a reliable integrated location-awareness solution. IoT devices are mainly relying on GNSS chip to deliver a service when no other RF signal is available. Hence, GNSS chip is expected to maintain a robust and reliable performance even in harsh environment. The harsh environment includes multipath environment, NLOS (Non-Line of Sight) GNSS signal reception, weak GNSS signal power reception, superimposed motion (e.g., arm swing, floating platform fluctuation …etc.), operation under freezing temperature, and interference. The targeted low-cost system design along with the expected harsh operational environment derived us to face exceptional GNSS signal reception condition. This paper discusses lessons learned, and challenges faced signal acquisition, reacquisition, and tracking for several IoT applications. Different operational modes are also explored to accommodate various use cases scenarios for several IoT applications. These different power consumption operational modes allow the GNSS chip to achieve an Ultra-low power consumption which indeed required for IoT applications. This paper provides an overview about the addressed GNSS chip fabricated in a 28nm FD-SOI process. We demonstrate a system performance and power consumption comparison of this chip along with other state-of-the-art GNSS chip. We compare the positioning performance for this device alongside another state-of-the-art GNSS modules and Novatel’s tightly coupled GNSS/INS (Inertial Navigation System) reference solution at numerous scenarios ranging from walking, running, and driving at several sites under different environment.

Internet of Things (IoT) is an emerging technology that consists of tiny sensors embedded with IoT devices used in e-Health, smart cities and assisted living. IoT devices are equipped with constrained power, memory, and computing capabilities, which creates new security challenges. By integrating sensors and wireless technologies, this cutting-edge technology automates numerous routine tasks in different domains. The main goal of advancing IoT in the existing healthcare system is to automate the monitoring of hospitals and patients’ health by spreading goodwill toward the IoT vision. IoT has gained popularity on practically all platforms that are available. This paper presents a remote patient health monitoring and diagnosis system that uses Message Queuing Telemetry Transport protocol (MQTT) and Internet of Things (IoT) devices with one time pad cryptography. The proposed plan offers end-to-end data secrecy for an IoT system using MQTT that enables e-health and mobility. Patients’ movements inside nursing homes and healthcare facilities are also managed safely without the need for periodic reconfigurations. Prior to communicating through the MQTT protocol, this secure crypto scheme encrypts the message on both the publisher and subscriber ends. According to the results, the proposed scheme is more efficient than delegation-based architecture in terms of security and lightweight communication. The proposed system established communication between patients and medical experts using IoT technologies to alleviate the hospital strain caused by congestion in medical treatment and enable an expressway of medical responses. According to our results the proposed scheme has low computational overhead compared to other solutions that use conventional encryption to guarantee robust protection against most of the IoT security attacks.

With the advancement of 5G mobile telecommunication, various IoT (Internet of Things) devices communicate massive amounts of data by being connected to wireless networks. Since this wireless communication is vulnerable to hackers via data leakage during communication, the transmitted data should be encrypted through block ciphers to protect the data during communication. In addition, in order to encrypt the massive amounts of data securely, it is essential to apply one of secure mode of operation. Among them, CTR (CounTeR) mode is the most widely used in industrial applications. However, these IoT devices have limited resources of computing and memory compared to typical computers, so that it is challenging to process cryptographic algorithms that have computation-intensive tasks in IoT devices at high speed. Thus, it is required that cryptographic algorithms are optimized in IoT devices. In other words, optimizing cryptographic operations on these IoT devices is not only basic but also an essential effort in order to build secure IoT-based service systems. For efficient encryption on IoT devices, even though several ARX (Add-Rotate-XOR)-based ciphers have been proposed, it still necessary to improve the performance of encryption for smooth and secure IoT services. In this article, we propose the first parallel implementations of CTR mode of ARX-based ciphers: LEA (Lightweight Encryption Algorithm), HIGHT (high security and light weight), and revised CHAM on the ARMv8 platform, a popular microcontroller in various IoT applications. For the parallel implementation, we propose an efficient data parallelism technique and register scheduling, which maximizes the usage of vector registers. Through proposed techniques, we process the maximum amount of encryption simultaneously by utilizing all vector registers. Namely, in the case of HIGHT and revised CHAM-64/128 (resp. LEA, revised CHAM-128/128, and CHAM-128/256), we can execute 48 (resp. 24) encryptions simultaneously. In addition, we optimize the process of CTR mode by pre-computing and using the intermediate value of some initial rounds by utilizing the property that the nonce part of CTR mode input is fixed during encryptions. Through the pre-computation table, CTR mode is optimized up until round 4 in LEA, round 5 in HIGHT, and round 7 in revised CHAM. With the proposed parallel processing technique, our software provides 3.09%, 5.26%, and 9.52% of improved performance in LEA, HIGHT, and revised CHAM-64/128, respectively, compared to the existing parallel works in ARM-based MCU. Furthermore, with the proposed CTR mode optimization technique, our software provides the most improved performance with 8.76%, 8.62%, and 15.87% in LEA-CTR, HIGHT-CTR, and revised CHAM-CTR, respectively. This work is the fastest implementation of CTR mode on ARMv8 architecture to the best of our knowledge.

The deployment of a client–server-based distributed intelligent system involves application development in both the network domain and the device domain. In the network domain, an application server (typically in the cloud) is deployed to execute the network applications. In the device domain, several Internet of Things (IoT) devices may be configured as, for example, wireless sensor networks (WSNs), and interact with each other through the application server. Developing the network and the device applications are tedious tasks that are the major costs for building a distributed intelligent system. To resolve this issue, a low-code or no-code (LCNC) approach has been purposed to automate code generation. As traditional LCNC solutions are highly generic, they tend to generate excess code and instructions, which will lack efficiency in terms of storage and processing. Fortunately, optimization of automated code generation can be achieved for IoT by taking advantage of the IoT characteristics. An IoT-based distributed intelligent system consists of the device domain (IoT devices) and the network domain (IoT server). The software of an IoT device in the device domain consists of the Device Application (DA) and the Sensor Application (SA). Most IoT LCNC approaches provide code generation in the network domain. Very few approaches automatically generate the DA code. To our knowledge, no approach supports the SA code generation. In this paper, we propose DeviceTalk, an LCNC environment for the DA and the SA code development. DeviceTalk automatically generates the code for IoT devices to speed up the software development in the device domain for a distributed intelligent system. We propose the DeviceTalk architecture, design and implementation of the code generation mechanism for the IoT devices. Then, we show how a developer can use the DeviceTalk Graphical User Interface (GUI) to exercise LCNC development of the device software.